Knowledge retrieval from PubMed abstracts and electronic medical records with the Multiple Sclerosis Ontology.

BACKGROUND: In order to retrieve useful information from scientific literature and electronic medical records (EMR) we developed an ontology specific for Multiple Sclerosis (MS). METHODS: The MS Ontology was created using scientific literature and expert review under the Protégé OWL environment. We developed a dictionary with semantic synonyms and translations to different languages for mining EMR. The MS Ontology was integrated with other ontologies and dictionaries (diseases/comorbidities, gene/protein, pathways, drug) into the text-mining tool SCAIView. We analyzed the EMRs from 624 patients with MS using the MS ontology dictionary in order to identify drug usage and comorbidities in MS. Testing competency questions and functional evaluation using F statistics further validated the usefulness of MS ontology. RESULTS: Validation of the lexicalized ontology by means of named entity recognition-based methods showed an adequate performance (F score = 0.73). The MS Ontology retrieved 80% of the genes associated with MS from scientific abstracts and identified additional pathways targeted by approved disease-modifying drugs (e.g. apoptosis pathways associated with mitoxantrone, rituximab and fingolimod). The analysis of the EMR from patients with MS identified current usage of disease modifying drugs and symptomatic therapy as well as comorbidities, which are in agreement with recent reports. CONCLUSION: The MS Ontology provides a semantic framework that is able to automatically extract information from both scientific literature and EMR from patients with MS, revealing new pathogenesis insights as well as new clinical information.

[Toxicoproteomics: first experiences in a BMBF-study]. / Toxikoproteomics: Erste Erfahrungen in einer BMBF-Studie.

The rapid development of molecular toxicology is providing innovative approaches to an improved investigation and recognition of toxic substances. Proteome analysis offers, with 2DE/MS (two-dimensional gel electrophoresis and mass spectrometry) and SELDI (surface enhanced laser desorption/ionisation), a promising discipline to classify molecular changes caused by toxic exposure. The Rat Liver Foci Bioassay (RLFB) is a detailed, well-described model for the investigation of liver carcinogenesis induced by chemical substances. Based on this model, we examined whether proteomic methods of molecular toxicology can be used for the early recognition of toxic and/or carcinogenic characteristics of toxic substances. In addition, identification and subsequent prevalidation of new hepatocellular biomarkers was performed, enabling better prediction of toxic and/or carcinogenic effects. This could lead to a more meaningful RLFB and thus to an improved risk assessment of chemicals. 2DE analysis in this study showed that deregulated proteins are assigned to mainly anabolic and catabolic metabolism pathways in the cell. Beyond this, individual proteins were identified which play a key role in the carcinogenic process. A comparison of the differentially expressed proteins in tissue from tumour-bearing animals and tissue derived from the start of the study revealed that protein expression changes (biomarkers) were already detectable shortly after exposure. In addition, analysis by SELDI clearly showed several differentially expressed proteins and/or derived masses. The spectra represented specific differences in tissues, which could be assigned to the same histopathological endpoints. With bioinformatics analysis it was possible to identify individual discriminating mass peaks, which were indicative of tumour formation. Group specific changes can be illustrated and/or represented in more detail with further cluster analysis methods. These results give hope for an improved prediction of hepatotoxicity and carcinogenicity by means of protein markers, which could in the future lead to a shortening of carcinogenicity studies and to a reduction in the use of experimental animals.